---
title: "MBON PIMCPA"
output: 
  flexdashboard::flex_dashboard:
    theme: lumen
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(leaflet)
library(ggplot2)
library(plotly)
library(spocc)
library(rinat)
library(mapr)


library(RColorBrewer)
palette(brewer.pal(8, "Set2"))


```

```{r dataread, message=FALSE, warning=FALSE, include=FALSE}
#READ both files metadata and percent covers 
setwd(paste0(getwd(),"/DATA"))#set new WD to folder DATA
PIMCPA.cover <- read.csv("percent_covers.csv")
PIMCPA.metadata <- read.csv("metadata.csv")
setwd("..")# original WD

colnames(PIMCPA.cover)[2] <-"Name" 
#Merge PIMCPA.metadata and PIMCPA.cover
PIMCPA<- merge(PIMCPA.metadata,PIMCPA.cover, by = "Name", all.x = TRUE) 

#Create long type dataframe 
library(reshape)
PIMCPA_long = melt(PIMCPA, id.vars = 1:21, measure.vars = 22:ncol(PIMCPA), variable_name = "CATAMI", value_name ="cover", na.rm = T)
#rename columns because the ontop command is not working 
colnames(PIMCPA_long)[23] <- "cover"

#Calculate mean, SD, SE for cover data by factors 
library(doBy)
Coverdata <- summaryBy(cover ~ CATAMI + strata,data=PIMCPA_long, FUN = function(x) { c(mean = mean(x),SD=sd(x),SE = sqrt(var(x)/length(x)))})

#add year from date info
PIMCPA$year <- lubridate::year(PIMCPA$Date)
photo_bydate = as.data.frame(table(PIMCPA$year,PIMCPA$site,PIMCPA$strata))
colnames(photo_bydate)=c("Fecha","Sitio","Estrato","n fotocuadrantes")  

```


Column1 {.tabset .tabset-fade}
-------

### Mapa Fotoquadrantes
```{r map, message=FALSE, warning=FALSE}
library(leaflet)
leaflet() %>% 
addMiniMap(toggleDisplay = T) %>% 
addProviderTiles(providers$Esri.WorldImagery) %>%
addCircleMarkers(data = PIMCPA, ~Longitude, ~Latitude,weight = 0.5,col = 'green', fillColor = 'green',radius = 4, fillOpacity = 0.5, stroke = T, label =PIMCPA$Name,group ='Fotocuadrantes')%>%
addLayersControl(overlayGroups = c("Fotocuadrantes"),options = layersControlOptions(collapsed = FALSE),position = 'topright')
```

### Phylum 
```{r donut}
library(plotly)
##numbers of observations by phylum 
p = Coverdata %>% plot_ly(labels = ~CATAMI, values=~cover.mean) %>% 
  add_pie(hole=0.6) %>% 
  layout(title = ~paste0("Porcentaje de cobertura promedio por categoria ")) 

plotly::config(p,displayModeBar = F) 
```



Column3{data-width=200}
-------

### Fotoquadrantes por fecha y sitio
```{r}
knitr::kable(as.data.frame(table(PIMCPA$year,PIMCPA$site,PIMCPA$strata)),col.names = c("Año","Sitio","Estrato","n"))
```